general
  • a

    adventurous-appointment-76656

    11/17/2022, 12:45 PM
    TCN work looks great btw, tossing this here in case there’s interest in it: https://lnkd.in/eBcGjh3V
  • a

    adventurous-appointment-76656

    11/17/2022, 12:48 PM
    We played around with neural ODEs a bit earlier this year but found them very slow to train and quite unstable, really excited about the new research direction!
  • s

    square-addition-87872

    11/17/2022, 10:35 PM
    Currently trying to migrate from
    fable
    to statsforecast in our production environment. However, I’m running into an issue with replication. Is it possible to
    boxcox
    transform prior to the forecasting step and back transform to produce the forecast mean rather than median? I’ve tried using the
    scipy
    python package, but it’s producing lower predictions than the R implementation. Any ideas / thoughts? Examples: ETS ->
    fable::ETS(fabletools::box_cox(qty + 1, lambda = 0.3), opt_crit = "mae")
    ARIMA ->
    fable::ARIMA((fabletools::box_cox(qty + 1, lambda = 0.4)) ~ PDQ(period = 13), stepwise = TRUE)
    THETA ->
    THETA(fabletools::box_cox(qty + 1, lambda = 0.1) ~ season(method = "additive"))
  • f

    famous-magazine-87421

    11/17/2022, 10:45 PM
    I think this will be a beginner question, what does levels do in the forecast function
  • f

    famous-magazine-87421

    11/17/2022, 10:45 PM
    fcsts_df = fcst.forecast(h=12, fitted=True, level=(90, 10))
  • f

    famous-magazine-87421

    11/17/2022, 10:45 PM
    Also if there's something to better learn how autoarima works
  • e

    elegant-spring-66661

    11/18/2022, 4:23 AM
    👋 Hello Nixtla team, wondering if you've come across AS197 estimator for SARMA models from this paper: Melard, G. "A fast algorithm for the exact likelihood of autoregressive-moving average." (1984). Thank you for the excellent numba SARMA implementation 🤝, it is definitely a dramatic improvement compared to existing alternatives! However, I found that it's still not fast enough as a benchmark method for less "well-behaved" time series: in particular with high frequency commodities time-series with high (p, q) and seasonal orders. This AS197 estimator, however, seems to solve this issue and the only open-source implementation at the moment is in Gretl I've been studying the performance of
    auto_arima
    (less numba compile time) and found a massive variance in training times (between 3-60 seconds) across time series as opposed to <5-10 seconds with
    auto_ets
    . After doing a meta-analysis of the fit-times against the optimal orders, I found that the current implementation is still quite slow at higher orders and with seasonal orders. Nevertheless, while looking through
    statsmodels
    , I found an issue that suggests using Chandrasekhar recursions (https://github.com/statsmodels/statsmodels/issues/6812) to dramatically speed up ARMA training times (up to >2-4x speed-up for higher orders with conditional SS). I've attached a screenshot from the paper that demonstrates this. Note: AS154 is the current Kalman filter implementation used in
    statsforecast
    and
    fable
    .
  • b

    blue-monitor-52767

    11/22/2022, 1:26 PM
    Hi. Sorry for bothering. Quick question: I tried running auto_arima_prophet in the same setup we have used Prophet for our forecasting and we have seen that auto_arima_prophet has 7x the running time for fit and predict, that Prophet has. Is there something we are doing wrong ? MSE is still 10% better for ARIMA, but still, I find it weird.
  • g

    gorgeous-refrigerator-40374

    11/25/2022, 11:59 AM
    Hi everyone, I joined this Nixtla community slack channel just now. I am working on hierarchical time series project. I am following this https://www.kaggle.com/code/konradb/ts-8-hierarchical-time-series/notebook to implement the hierarchical model.
  • g

    gorgeous-refrigerator-40374

    11/25/2022, 12:00 PM
    I am using the following command to install the required libraries.pip3 install hierarchicalforecast statsforecast datasetsforecast
  • g

    gorgeous-refrigerator-40374

    11/25/2022, 12:02 PM
    Facing some errors and issues. Please someone from the community who has faced these issues or any kind of errors can help me. It is blocking my workflow since past 4 days.
  • g

    gorgeous-refrigerator-40374

    11/25/2022, 2:20 PM
    I tried to install hierarchicalforecast library using the following command.pip3 install hierarchicalforecast
  • g

    gorgeous-refrigerator-40374

    11/25/2022, 2:21 PM
    I got this error.
  • g

    gorgeous-refrigerator-40374

    11/25/2022, 2:21 PM
    Please someone help me if you know this.
  • b

    blue-monitor-52767

    11/25/2022, 2:25 PM
    Hi. Anybody had any issues with LightGBM regressor/ETS returning the same values for each forecasting step ?
  • f

    famous-house-83133

    11/25/2022, 7:03 PM
  • g

    gentle-helicopter-20098

    11/29/2022, 1:12 AM
    Hey! As part of our #reinventforecasting week, we released the MSTL model. Show some love community ❤️ https://www.reddit.com/r/datascience/comments/z7etr8/on_the_law_of_similarity_or_how_10000yearold/
  • f

    famous-house-83133

    12/01/2022, 7:37 PM
  • f

    famous-house-83133

    12/01/2022, 7:38 PM
    We created some memes that we did end up not using: feel free to take them lol
  • v

    victorious-fall-43699

    12/01/2022, 7:42 PM
    Ha, I for one love these memes @famous-house-83133
  • a

    ambitious-sandwich-21302

    12/01/2022, 8:31 PM
  • a

    ambitious-sandwich-21302

    12/01/2022, 8:32 PM
    @famous-house-83133 what is this DOT thing, is there an article link please somewhere?
  • f

    famous-house-83133

    12/01/2022, 8:32 PM
    Dynamic Optimzed Theta
  • f

    famous-house-83133

    12/01/2022, 8:33 PM
    The theta family of models has been shown to perform well in various datasets such as M3.
    StatsForecast
    includes four models of the family:
    Theta
    ,
    OptimizedTheta
    ,
    DynamicTheta
    ,
    DynamicOptimizedTheta
    . The implementation is based on the work of Jose A. Fioruccia, Tiago R. Pellegrini, Francisco Louzada, Fotios Petropoulos, and Anne B.Koehlerf, and optimized using numba.
  • f

    famous-house-83133

    12/01/2022, 8:33 PM
    It was just to long to write jajajaja
  • a

    ambitious-sandwich-21302

    12/01/2022, 8:38 PM
    I see, great. I think Theta author is someone else though. These two Assimakopoulos, V., & Nikolopoulos, K. (2000). The theta model: a decomposition approach to forecasting. International journal of forecasting,
  • v

    victorious-fall-43699

    12/01/2022, 8:46 PM
    Thought this post would be relevant for the Nixtla community here, some chatter about it at H2O.ai : https://huggingface.co/blog/time-series-transformers
  • a

    ambitious-sandwich-21302

    12/01/2022, 9:04 PM
    @famous-house-83133 is the 4 models combo the same that Svetunkov & Co used in M4? From what I recollect they branded is with name “SCUM” 😀 Or Nixtla combo is a bit different? This is a link to their paper https://researchportal.bath.ac.uk/en/publications/a-simple-combination-of-univariate-models
  • j

    jolly-monkey-88302

    12/01/2022, 11:12 PM
    Hey there, just joined, not sure if we’re supposed to present ourselves in this slack but I’ll do it anyway 🙂 I work on an anomaly detection system called ThirdEye. We use a bunch of forecasting-based method for anomaly detection, even though I mostly work on matrix profile these days. Just discovered your libs with Statistical vs Deep Learning forecasting methods piece of work, and will definitely dig it. Thanks for the awesome work!